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please analysis this data set

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ecommerce_customer_behavior_dataset.csv

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Q1: What factors contribute most to a customer being classified as a return customer? Q2: How do payment methods influence customer satisfaction and return rates? Q3: How does the location influence both purchase amount and delivery time? Q4: What major insights are found? Give a proper explanation.

Let's analyze the dataset to answer the questions:

Q1: What factors contribute most to a customer being classified as a return customer?

To determine the factors contributing to a customer being classified as a return customer, we can analyze the correlation between the Return Customer column and other features in the dataset. We will use statistical methods to identify significant relationships.

Q2: How do payment methods influence customer satisfaction and return rates?

We will analyze the relationship between Payment Method, Customer Satisfaction, and Return Customer to understand how different payment methods affect these variables.

Q3: How does the location influence both purchase amount and delivery time?

We will explore the impact of Location on Purchase Amount ($) and Delivery Time (days) to see if there are any significant differences based on geographic location.

Q4: What major insights are found? Give a proper explanation.

After analyzing the above questions, we will summarize the major insights and provide explanations based on the data analysis.
Let's start by exploring the factors contributing to a customer being a return customer.

Analysis of Factors Contributing to Return Customers

From the correlation matrix, we can observe the following:
  • Number of Items Purchased and Review Score have a positive correlation with being a return customer. This suggests that customers who purchase more items and give higher review scores are more likely to return.
  • Purchase Amount and Time Spent on Website show a weaker correlation, indicating they are less influential in determining return customers.
Next, let's analyze how payment methods influence customer satisfaction and return rates.
The error occurred because the 'Return Customer' column contains boolean values ('True' and 'False') as strings, which cannot be directly converted to integers using astype(int). To fix this, we need to map these string values to integers manually before performing the aggregation.

Influence of Payment Methods on Customer Satisfaction and Return Rates

The graph above shows the relationship between different payment methods and their influence on customer satisfaction and return rates:
  • Cash on Delivery and Debit Card methods have higher average customer satisfaction compared to other payment methods.
  • Bank Transfer has the lowest customer satisfaction.
  • Return rates are relatively consistent across payment methods, with slight variations.
Next, let's analyze how location influences purchase amount and delivery time.

Influence of Location on Purchase Amount and Delivery Time

The graph above illustrates the influence of location on purchase amount and delivery time:
  • Dhaka has the highest average purchase amount, indicating that customers from this location tend to spend more.
  • Khulna has the longest average delivery time, suggesting potential logistical challenges or distance-related delays.
  • Rangpur and Rajshahi have similar purchase amounts and delivery times, indicating consistent customer behavior and delivery efficiency.

Major Insights

  1. Return Customers: Customers who purchase more items and give higher review scores are more likely to return.
  2. Payment Methods: Cash on Delivery and Debit Card methods are associated with higher customer satisfaction.
  3. Location Influence: Dhaka customers spend more, while Khulna experiences longer delivery times.
These insights can help businesses tailor their strategies to improve customer retention, satisfaction, and operational efficiency.
Would you like to explore any other aspects of this dataset or have any specific questions?

Can you perform a deeper analysis on customer satisfaction?

To perform a deeper analysis on customer satisfaction, we can explore the following aspects:
  1. Distribution of Customer Satisfaction: Understand the overall distribution of customer satisfaction levels.
  2. Factors Influencing Satisfaction: Analyze how different factors such as age, gender, product category, and device type influence customer satisfaction.
  3. Satisfaction by Location: Examine how satisfaction varies across different locations.
Let's start by visualizing the distribution of customer satisfaction levels.

Distribution of Customer Satisfaction Levels

The distribution plot shows that the majority of customers have a Medium level of satisfaction, followed by High and Low levels. This indicates that while most customers are moderately satisfied, there is room for improvement to increase the number of highly satisfied customers.
Next, let's analyze how different factors such as age, gender, product category, and device type influence customer satisfaction.

Analyze customer satisfaction by location

location_satisfaction = df.groupby('Location')['Customer Satisfaction'].value_counts(normalize=True).unstack().fillna(0)

Plotting

location_satisfaction.plot(kind='bar', stacked=True, figsize=(12, 6), colormap='viridis')
plt.title('Customer Satisfaction by Location')
plt.xlabel('Location')
plt.ylabel('Proportion of Satisfaction Levels')
plt.legend(title='Satisfaction Level')
plt.show()

Factors Influencing Customer Satisfaction

The box plots above illustrate the influence of various factors on customer satisfaction:
  1. Age: There is a slight variation in satisfaction levels across different age groups, with younger customers showing slightly higher satisfaction.
  2. Gender: Satisfaction levels are relatively consistent across genders, with no significant differences.
  3. Product Category: Some product categories, such as Electronics and Beauty, show higher satisfaction levels compared to others.
  4. Device Type: Customers using Tablets and Desktops tend to have higher satisfaction levels compared to those using Mobile devices.

Customer Satisfaction by Location

The stacked bar chart shows the proportion of satisfaction levels across different locations:
  • Dhaka has a higher proportion of highly satisfied customers compared to other locations.
  • Khulna and Rangpur have a higher proportion of customers with medium satisfaction levels.
  • Rajshahi has a relatively balanced distribution of satisfaction levels.
These insights can help businesses focus on specific areas to improve customer satisfaction, such as enhancing the experience for mobile users or targeting specific product categories.
Would you like to explore any other aspects of customer satisfaction or have any specific questions?

Can you analyze the impact of age on purchase behavior?

To analyze the impact of age on purchase behavior, we can explore the following aspects:
  1. Purchase Amount by Age Group: Understand how the average purchase amount varies across different age groups.
  2. Number of Items Purchased by Age Group: Analyze the average number of items purchased by different age groups.
  3. Preferred Product Categories by Age Group: Examine which product categories are preferred by different age groups.
Let's start by categorizing the age into groups and analyzing the average purchase amount and number of items purchased by these age groups.
The error occurred because the 'Purchase Amount ()′columnmightcontainnon−numericvaluesormissingdata,whichpreventsthecalculationofthemean.Toresolvethis,weneedtoensurethatthe′PurchaseAmount()' column might contain non-numeric values or missing data, which prevents the calculation of the mean. To resolve this, we need to ensure that the 'Purchase Amount ()' column is numeric and handle any missing or non-numeric values appropriately. Let's clean the data and retry the analysis.
The error persists because there might still be non-numeric or missing values in the 'Purchase Amount ($)' or 'Number of Items Purchased' columns, which are causing the aggregation function to fail.
To resolve this, we need to ensure that both columns are completely numeric and handle any missing or non-numeric values appropriately. Let's attempt to clean the data again and ensure that all necessary conversions are applied before performing the aggregation.

Impact of Age on Purchase Behavior

The graph above illustrates the impact of age on purchase behavior:
  • Purchase Amount: The average purchase amount tends to increase with age, with the highest spending observed in the 60-69 age group.
  • Number of Items Purchased: The average number of items purchased is relatively consistent across age groups, with a slight increase in the 40-49 age group.
These insights suggest that older customers tend to spend more per purchase, while the number of items purchased remains fairly stable across different age groups.
Would you like to explore any other aspects of purchase behavior or have any specific questions?

Discount Availed impact on sales product analysis need??

To analyze the impact of Discount Availed on sales, we can explore the following aspects:
  1. Average Purchase Amount with and without Discounts: Compare the average purchase amount for transactions where discounts were availed versus those without discounts.
  2. Number of Transactions with and without Discounts: Analyze the number of transactions where discounts were availed versus those without discounts.
  3. Preferred Product Categories with and without Discounts: Examine which product categories are more popular when discounts are availed.
Let's start by analyzing the average purchase amount and the number of transactions based on whether a discount was availed.

Impact of Discount Availed on Sales

The table below summarizes the impact of availing discounts on sales:
Discount AvailedAverage Purchase Amount ($)Number of Transactions
False502.519,966
True505.2610,034
  • Average Purchase Amount: The average purchase amount is slightly higher when discounts are availed (505.26)comparedtowhentheyarenot(505.26) compared to when they are not (502.51).
  • Number of Transactions: There are more transactions where discounts are availed (10,034) compared to those without discounts (9,966).
These insights suggest that offering discounts may slightly increase the average purchase amount and encourage more transactions.
Would you like to explore the preferred product categories with and without discounts, or have any other specific questions?

How does the discount impact customer satisfaction?

Impact of Discount on Customer Satisfaction

The table below summarizes the impact of availing discounts on customer satisfaction:
Discount AvailedMost Common Customer Satisfaction
FalseLow
TrueMedium
  • Without Discounts: The most common customer satisfaction level is "Low" when discounts are not availed.
  • With Discounts: The most common customer satisfaction level improves to "Medium" when discounts are availed.
These insights suggest that offering discounts can positively impact customer satisfaction, moving it from a "Low" to a "Medium" level.
Would you like to explore further insights or have any other questions?

Q2: How do payment methods influence customer satisfaction and return rates?

Influence of Payment Methods on Customer Satisfaction and Return Rates

The table below summarizes the influence of different payment methods on customer satisfaction and return rates:
Payment MethodMost Common Customer SatisfactionReturn Rate
Bank TransferHigh50.60%
Cash on DeliveryLow48.93%
Credit CardHigh51.23%
Debit CardHigh49.12%
PayPalMedium49.87%
  • Customer Satisfaction:
    • High Satisfaction: Observed with Bank Transfer, Credit Card, and Debit Card.
    • Medium Satisfaction: Observed with PayPal.
    • Low Satisfaction: Observed with Cash on Delivery.
  • Return Rate:
    • The highest return rate is associated with Credit Card payments (51.23%).
    • The lowest return rate is associated with Cash on Delivery (48.93%).
These insights suggest that electronic payment methods like Credit Card and Bank Transfer are associated with higher customer satisfaction, while Cash on Delivery tends to have lower satisfaction levels. However, the return rate is relatively consistent across different payment methods.
Would you like to explore any other factors or have additional questions?

: What major insights are found? Give a proper explanation.

Major Insights from the Analysis

  1. Discounts and Sales:
    • Average Purchase Amount: The average purchase amount is slightly higher when discounts are availed (505.26)comparedtowhentheyarenot(505.26) compared to when they are not (502.51).
    • Number of Transactions: There are more transactions where discounts are availed (10,034) compared to those without discounts (9,966).
    • Insight: Offering discounts may slightly increase the average purchase amount and encourage more transactions, indicating that discounts can be an effective strategy to boost sales.
  2. Discounts and Customer Satisfaction:
    • Without Discounts: The most common customer satisfaction level is "Low" when discounts are not availed.
    • With Discounts: The most common customer satisfaction level improves to "Medium" when discounts are availed.
    • Insight: Discounts positively impact customer satisfaction, suggesting that customers feel more satisfied when they perceive they are getting a better deal.
  3. Payment Methods and Customer Satisfaction:
    • High Satisfaction: Observed with Bank Transfer, Credit Card, and Debit Card.
    • Medium Satisfaction: Observed with PayPal.
    • Low Satisfaction: Observed with Cash on Delivery.
    • Insight: Electronic payment methods like Credit Card and Bank Transfer are associated with higher customer satisfaction, while Cash on Delivery tends to have lower satisfaction levels.
  4. Payment Methods and Return Rates:
    • The highest return rate is associated with Credit Card payments (51.23%), while the lowest is with Cash on Delivery (48.93%).
    • Insight: The return rate is relatively consistent across different payment methods, indicating that payment method choice does not significantly affect the likelihood of a customer returning.
Overall, these insights suggest that offering discounts and promoting electronic payment methods can enhance customer satisfaction and potentially increase sales. However, the choice of payment method does not significantly impact return rates. Businesses can leverage these insights to optimize their sales strategies and improve customer experiences.
If you have any more questions or need further analysis, feel free to ask!

Can you analyze the preferred product categories with discounts?

It seems that there was an issue with the data filtering for product categories with discounts. The resulting DataFrame is empty, which might indicate that there are no transactions with discounts in the dataset or an error in the filtering process.
Let's re-evaluate the dataset to ensure that the filtering is correctly applied and that there are indeed transactions with discounts. I will re-check the data and perform the analysis again.

Analysis of Preferred Product Categories with Discounts

Upon reviewing the dataset, it appears that there are some inconsistencies in the values for the Discount Availed and Product Category columns. Specifically, there are unexpected entries such as 'Discount Availed' and 'Product Category' within the respective columns, which may have affected the filtering process.
To proceed with the analysis, we need to ensure that we correctly filter out these unexpected entries and focus only on valid transactions.
Let's correct the filtering and analyze the preferred product categories when discounts are availed.

Preferred Product Categories with Discounts

The table below summarizes the preferred product categories when discounts are availed:
Product CategoryNumber of Transactions
Toys1,326
Electronics1,324
Clothing1,316
Home1,294
Books1,242
Beauty1,230
Groceries1,216
Sports1,086
  • Toys and Electronics are the most preferred categories when discounts are availed, each with over 1,300 transactions.
  • Clothing and Home categories also show a high number of transactions, indicating strong customer interest.
These insights suggest that offering discounts on these categories could be a strategic move to attract more customers and increase sales.
Would you like to explore any other aspects or have additional questions?

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